JP6405976B2 - Crop taste estimation device and crop taste estimation program - Google Patents

Description

  The present invention relates to a crop taste estimation device and a crop taste estimation program.

  Without expertise and experience, it is difficult to judge the taste and harvest time of crops from the appearance. On the other hand, if crops are harvested at an appropriate time, crops that are traded at high prices can be shipped. For example, even an inexperienced agricultural beginner can accurately estimate the taste and harvest time of crops. There is a growing interest in and methods.

  For example, in Patent Document 1, a rice taste evaluation apparatus that uses a sample rice to measure a plurality of predetermined components including moisture that affect the taste of rice without performing heat treatment or chemical treatment, and evaluates the taste of the ingredients. Has been proposed. Further, in Patent Document 2, a yield of a crop and a sugar content rate are predicted from the light reflectance of the crop obtained from remote sensing data, and a sugar amount is calculated from the obtained root yield and sugar content rate. Yield prediction methods have been proposed.

Japanese Patent Laid-Open No. 5-232017 JP 2010-166851 A

  However, the technique disclosed in Patent Document 1 has a problem that it is difficult to estimate the taste of a growing crop without taking a sample because it is an apparatus that performs taste evaluation using sample rice. Further, the technique disclosed in Patent Document 2 has a problem that accurate quantitative taste evaluation is difficult because the amount of components other than sugar cannot be predicted.

  The present invention has been made in view of such circumstances, and an object thereof is to accurately and quantitatively estimate the taste of a growing crop without taking a sample.

  In order to solve the above problems, a crop taste estimation device according to the present invention is a device for estimating the taste of a crop, using measurement data that is environmental information surrounding the crop, and sweetness, bitterness, sourness, salty taste, Estimating the value of the taste component amount indicating the quality of at least one selected from the basic taste elements of umami and at least one of the taste elements selected from the secondary taste elements of texture, moisture and aroma The taste value of each taste element indicating how close the taste component amount of each taste element is to the target value using the taste component amount estimated by the taste ingredient amount estimation means, the taste ingredient amount estimation means Taste estimation means for estimating the taste, and taste estimation means for obtaining an estimated value of the taste of the crop using the taste value of each taste element estimated by the taste value estimation means.

  According to the present invention, since the amount of taste component is estimated using measurement data that affects the amount of taste component of each taste element of the crop, the taste is not collected from the growing crop without taking a sample for taste estimation. Can be guessed. In addition, one kind of basic taste element that contributes to the determination of the taste of crops and one kind from secondary taste elements are selected, and the taste is evaluated numerically as a taste value. can do.

  The taste component amount estimation means uses the first regression equation obtained by regression analysis of the relationship between the first reference value that is the taste component amount indicating the quality of each taste element and the second reference value that is the measurement data. You may comprise so that the value of the amount of taste ingredients may be estimated. In this case, since the amount of taste component is estimated using measurement data that affects the amount of taste component of each taste element of the crop, the taste is estimated without taking a sample for taste estimation from the growing crop. be able to.

  The taste value estimation means is a taste component amount indicating the quality of each taste element and is different from the first reference value, and indicates how close the third reference value is to the target value. You may comprise so that a taste value may be estimated using the 2nd regression type which regression-analyzed the relationship with the 4th reference value which is the taste value of a taste element. In this case, since the amount of taste component is estimated using measurement data that affects the amount of taste component of each taste element of the crop, the taste is estimated without taking a sample for taste estimation from the growing crop. be able to.

  Preferably, the first to fourth reference values are past values. In this case, since the value of the taste component amount of each taste element and the taste value of each taste element are estimated by the first and second regression equations obtained by regression analysis using past values, Taste can be estimated without taking a sample for taste estimation.

  Preferably, the amount of taste component of each taste element is sugar content for sweetness, nitrate nitrogen content and polyphenol content for bitterness, citric acid content for sourness, salt content for saltiness, amino acid content for umami taste, and pectin whose texture is decomposed It is preferable that the amount, the amount of amylose / amylopectin, or the protein, the amount of water is the amount of water, and the scent is monoterpene or ester. In this case, the taste can be accurately estimated regardless of the type of the crop by using a highly versatile component as the amount of the taste component. As a result, the application range of crops whose taste can be estimated is expanded, so that convenience can be greatly improved.

  More preferably, the value of the sugar content is the relationship between the first reference value of the sugar content and the accumulated sunshine duration, the accumulated potassium amount in the soil, the accumulated value of the average temperature of the night, and the accumulated value of the average temperature of the day. The value of nitrate nitrogen amount is estimated by using a regression analysis of the relationship between the first reference value of the nitrate nitrogen amount and the accumulated nitrogen amount in the soil. The value of the amount of polyphenol is estimated using a regression equation, the value of the polyphenol amount is estimated using a first regression equation obtained by regression analysis of the relationship between the first reference value of the amount of polyphenol and the integrated value of integrated light quantity and average temperature, The value of the amount of citric acid is estimated using a first regression equation obtained by regression analysis of the relationship between the first reference value of the amount of citric acid and the elapsed time after flowering. It is estimated using the first regression equation that is a regression analysis of the relationship between the first reference value and the accumulated soil salinity. The value of the amino acid amount is the relationship between the first reference value of the amino acid amount and the accumulated value of soil accumulated nitrogen amount, soil accumulated amino acid amount, accumulated sunshine duration, soil accumulated potassium amount, and daytime average temperature. The amount of pectin, amylose / amylopectin, or protein estimated and degraded using the first regression equation obtained by regression analysis is expressed as the first reference value of the amount of pectin decomposed and the elapsed time after fruit set. The relationship between the first reference value of the amount of amylose and amylopectin and the accumulated sunshine duration, the accumulated value of the average night temperature, the accumulated potassium amount in the soil, and the accumulated value of the average daily temperature, or the Using the first regression equation that regressed the relationship between the reference value of 1 and the accumulated nitrogen amount in the soil, the accumulated amino acid amount in the soil, the accumulated potassium amount in the soil, the accumulated value of the daytime average temperature, and the accumulated sunshine duration The water content is estimated The first reference value of the water content is estimated using a first regression equation that is a regression analysis of the relationship between the accumulated water content in the soil and the accumulated salt content in the soil, and the value of the monoterpene or the ester content is When the first reference value of the monoterpene and the elapsed time after ripening or the relationship between the first reference value of the ester amount and the elapsed time after ripening is estimated using the first regression equation Good. In this case, since measurement data having a large influence on the generation of the taste component amount is used according to the taste component amount of each taste element, it becomes possible to accurately estimate the taste component amount, and further enhance the taste of the crop. It can be estimated with high accuracy.

  Preferably, the apparatus further comprises position information acquisition means for acquiring position information of the crop, and the taste component amount estimation means is the amount of taste component of each taste element corresponding to the measurement data based on the position information measured by the position information acquisition means. It is good to guess the value of. In this case, the accuracy of estimating the taste of the crop can be further improved by using the measurement data based on the position information of the crop without performing measurement such as instrument analysis that requires specialized knowledge.

  Preferably, display means for displaying the estimated value of the taste of the crop is further provided. In this case, since it is possible to easily inform the user of the estimated taste value, the convenience can be improved.

  A crop taste estimation program according to the present invention selects a computer from the basic taste elements of sweet taste, bitter taste, sour taste, salty taste, and umami taste, using measurement data that is environmental information surrounding the crop to guess the taste of the crop. Taste component amount estimation means for estimating the value of the taste component amount indicating the quality of each taste element selected from at least one kind of taste element, and at least one taste element selected from texture, moisture, and aroma Taste value estimation means for estimating the taste value of each taste element indicating how close the taste component amount value of each taste element is to the target value using the taste component amount estimated by the ingredient amount estimation means Using the taste value of each taste element estimated by the taste value estimation means, it functions as a taste estimation means for obtaining an estimated value of the taste of the crop.

  According to the present invention, since the amount of taste component is estimated using measurement data that affects the amount of taste component of each taste element of the crop, the taste is not collected from the growing crop without taking a sample for taste estimation. Can be guessed. In addition, one kind of basic taste element that contributes to the determination of the taste of crops and one kind from secondary taste elements are selected, and the taste is evaluated numerically as a taste value. can do.

  According to the present invention, the taste of a growing crop can be accurately and quantitatively estimated without taking a sample.

It is a block diagram which shows the crop taste estimation apparatus which concerns on Embodiment 1 of this invention with a database. It is an image figure which shows the data preserve | saved at the database. It is an image figure which shows the list which attached | subjected the taste value of each taste element with respect to the taste of agricultural products, and the taste of agricultural products. It is a flowchart which shows the processing operation | movement of the crop taste estimation apparatus which concerns on Embodiment 1 of this invention. It is a block diagram which shows the crop taste estimation apparatus which concerns on Embodiment 2 of this invention with a database. It is a block diagram which shows the crop taste estimation apparatus which concerns on Embodiment 3 of this invention with a database. It is an example which the display part of the crop taste estimation apparatus which concerns on Embodiment 3 of this invention displays a log | history in bar shape. It is an example in which the display part of the crop taste estimation device according to Embodiment 3 of the present invention displays the history in a graph.

  DESCRIPTION OF EMBODIMENTS Embodiments (embodiments) for carrying out the present invention will be described in detail with reference to the drawings. The present invention is not limited to the following embodiments. The constituent elements described below include those that can be easily assumed by those skilled in the art and those that are substantially the same. Furthermore, the constituent elements described below can be appropriately combined. In addition, various omissions, substitutions, or changes of components can be made without departing from the scope of the present invention. In the description, the same reference numerals are used for the same elements or elements having the same function, and redundant description is omitted.

(Embodiment 1)
First, with reference to FIG. 1, the structure and processing operation | movement of the crop taste estimation apparatus 1 which concern on Embodiment 1 of this invention are demonstrated. FIG. 1 is a block diagram showing a crop taste estimation device according to Embodiment 1 of the present invention together with a database. The crop taste estimation device 1 can be realized by hardware such as a computer CPU (Central Processing Unit), and is realized by a program having a data transmission / reception function in terms of software. In FIG. 1 and the like, functional blocks realized by their cooperation are depicted. Therefore, these functional blocks can be realized in various forms by a combination of hardware and software. Here, the configuration of each functional block will be mainly described. Specific processing contents will be described in detail after the description of the configuration.

  As shown in FIG. 1, the crop taste estimation device 1 includes a communication unit 1a, a storage unit 1b, and a calculation unit 1c. Examples of the crop targeted by the crop taste estimation device 1 include rice, vegetables, and fruits. Here, the taste of agricultural products is based on the five basic taste elements of sweetness, bitterness, sourness, salty taste, and umami, which are the basic elements of taste, and three secondary taste elements of texture, moisture, and aroma. Composed. Each of these taste elements contributes differently depending on the type of crop. The crop taste estimation device 1 according to this embodiment is mainly intended to estimate the taste of this crop.

  The communication unit 1a is configured to be able to communicate with the database 2 via a network. Specifically, in order to estimate the taste of the crop, it has a function of reading out and acquiring necessary data from the database 2 in response to a request from the calculation unit 1c described later, and storing it in the storage unit 1b described later. The network may be an Internet network, or a private communication network such as a LAN (Local Area Network), a mobile phone network, a telephone line network, an ISDN (Integrated Services Digital Network), or a public communication network such as a dedicated line.

  Here, the data stored in the database 2 will be described in detail with reference to FIG. FIG. 2 is an image diagram showing data stored in the database. The database 2 stores the first region for storing soil component information and weather information among the measurement data that is environment information surrounding the crop, and the growth status information among the measurement data that is environment information surrounding the crop. A second region, a third region for storing a coefficient of each taste element of the crop, a fourth region for storing a first regression equation for calculating a taste component amount of each taste element of the crop, There are five main areas: a fifth area for storing the second regression equation for calculating the taste value of each taste element. The hardware constituting the database 2 is not particularly limited as long as it can record and save data, and includes, for example, a recording medium such as a hard disk. The installation location of the database 2 may be, for example, on a cloud network, a network server, or in a computer. In addition, as the representation of the place, an identifier that can distinguish the fields such as A and B shown in FIG. 2 may be used, or a latitude or longitude may be used. Further, the database 2 does not have to be consolidated into one. For example, soil component information is obtained from its own database storing field sensor values, and weather information is obtained from the Japan Meteorological Agency database. May be. Furthermore, the unit of data storage date and time may be every day or every 12 hours.

  First, the first area of the database 2 will be described in detail. As shown in FIG. 2, the first area stores measurement data at the location of the crop whose taste is to be estimated. The measurement data is not particularly limited as long as it is data indicating environmental factors that affect the amount of taste components of each taste element. In the present embodiment, soil component information and weather information data are stored. As soil component information, potassium content [mg / dry soil 100 g], amino acid content [mg / dry soil 100 g], nitrogen content [mg / dry soil 100 g], salt content [mg / dry soil 100 g], water content [ %]. The weather information includes measured values of sunshine duration [h], night average temperature [° C.], daytime average temperature [° C.], light intensity [lx], and average temperature [° C.]. These data store values obtained from sensors and measuring instruments installed in the fields. For example, the average night temperature can be obtained by storing the average temperature measured by a thermometer every hour from sunset to sunrise. When these data are used as integrated values, the sum of data from the starting point of integration to the time of taste estimation is calculated. Examples of the starting point of integration include the sowing date, germination date, and rice planting date. For example, let us consider a case where it is desired to guess the taste of rice growing in the place A on 9/15 shown in FIG. Assuming that 5/10 of the date of rice planting is the starting date of calculation, soil component information and weather information of location A from 5/10 of the date of rice planting to 9/15 of the taste estimation date are acquired. The integrated value is obtained by calculating the sum of each measured value from 5/10 to 9/15.

  Next, the second area of the database 2 will be described in detail. As shown in FIG. 2, the 2nd area | region has preserve | saved the growth condition information corresponding to the name and variety of the planted plant for every place. In the present embodiment, the growth status information includes flowering date / time and fruiting date / time. When the elapsed time after flowering is used for taste estimation, the elapsed time from the acquired flowering date and time to the taste estimation is calculated. When the elapsed time after fruiting is used for taste estimation, the elapsed time from the acquired fruiting date and time to the taste estimation is calculated. This growth status information may be acquired using image recognition, or may be acquired from information visually confirmed by a measurer. In addition, as the growth status information, it is preferable to store density information of crops such as how densely seedlings are grown and how many fruit trees are planted per square meter. In this case, the taste can be estimated with higher accuracy. This density information may be input manually or automatically measured by image determination or the like.

  Next, the third area of the database 2 will be described in detail. As shown in FIG. 2, the third area stores the basic values of the coefficients of the taste elements corresponding to the names and varieties of the crops planted, and the correction values of the coefficients. Here, the basic value of the coefficient of each taste element is a coefficient corresponding to the degree of contribution of each taste element to the taste of the crop. The coefficient correction value is a value that enables the coefficient of a certain crop in a certain place to be changed independently according to taste and target taste. When changing the coefficient, the coefficient correction value is added to or subtracted from the coefficient basic value.

  Next, the fourth area of the database 2 will be described in detail. As shown in FIG. 2, the fourth region stores the first regression equation corresponding to the name and variety of the planted plant. The first regression equation is a regression equation for estimating the taste component amount of each taste element, and is at least one selected from basic taste elements of sweet taste, bitter taste, sour taste, salty taste, and umami taste, texture, moisture The first reference value, which is the amount of the taste component indicating the quality of each taste element selected from the sub-flavor elements of the fragrance, and the measurement data that affects the amount of the taste component of each taste element It is an equation obtained by regression analysis of the relationship with the reference value of 2. In other words, it is an expression with the taste component amount of each taste element as an objective variable and the measurement data as an explanatory variable. The first regression equation is preferably stored in correspondence with the density information of the crop. In this case, the taste can be estimated with higher accuracy.

  Next, the fifth area of the database 2 will be described in detail. As shown in FIG. 2, the fifth region stores a second regression equation corresponding to the name and variety of the planted plant for each place. The second regression equation is a regression equation for estimating the taste value of each taste element, which is the amount of the taste component of each taste element and is different from the first reference value, 3 is an expression obtained by regression analysis of the relationship with the fourth reference value, which is the taste value of each taste element, indicating how close the reference value of 3 is to the target value. In other words, the taste value of each taste element is an objective variable, and the taste component amount of each taste element is an explanatory variable. The data from the first area to the fifth area is related to the location of the crop whose taste is to be estimated. Here, the first to fourth reference values are past values. Specifically, the first reference value is a value indicating the amount of taste component of each taste element of a growing crop that changes with changes in the surrounding environment. However, the first reference value is a value obtained by actually measuring the amount of taste components of each taste element of the growing crop, not the growing crop to be estimated. The third reference value is a value obtained by actually measuring the amount of the taste component of each taste element of the crop harvested in the past, and is a value indicating the amount of the taste component when the taste value is a certain value. That is, the first reference value and the third reference value are both values representing the amount of taste components of each taste element of the crop, but mean different values. The details of the taste component amount of each taste element, measurement data, and the taste value of each taste element will be described later.

  The storage unit 1b is a storage area for holding various data. Specifically, the communication unit 1a plays a role of saving data acquired from the database 2 and a value of data calculated by the calculation unit 1c described later. A flash memory or the like may be used as the storage unit 1b.

  The calculation unit 1c instructs the communication unit 1a to read out data necessary for estimating the taste of the crop from the database 2, and uses the data read from the database 2 stored in the storage unit 1b to It assumes the function of guessing. The calculation unit 1c includes a taste component amount estimation unit 11c, a taste value estimation unit 12c, and a taste estimation unit 13c. Examples of the arithmetic unit 1c include a CPU and a DSP (Digital Signal Processor). In addition, the memory | storage part 1b and the calculating part 1c may be comprised integrally.

  The taste component amount estimation unit 11c functions as a taste component amount estimation unit that estimates the value of the taste component amount of each taste element at the time point to be measured using the first regression equation read from the database 2 by the communication unit 1a. have. Here, the taste component amount estimation unit 11c is configured to be able to estimate the past value and the current value of the taste component amount of each taste element. That is, the value of the taste component amount of each taste element at the time point (past or present) that the user wants to measure can be estimated. For example, when estimating the past value of the taste component amount of each taste element, the past value of the measurement data is substituted into the first regression equation, and the taste of each taste element corresponding to the past value of the measurement data The value of the component amount will be estimated. On the other hand, when estimating the current value of the taste component amount of each taste element, the current value of the measurement data is substituted into the first regression equation, and the taste of each taste element corresponding to the current value of the measurement data The amount of ingredients will be estimated. The value of the taste component amount of each taste element estimated by the taste component amount estimation unit 11c is stored in the storage unit 1c. The taste component amount estimation unit 11c includes at least one selected from basic taste elements of sweetness, bitterness, sourness, salty taste, and umami, and at least one selected from secondary taste elements of texture, moisture, and aroma. The taste component amount of each taste element may be estimated, and the taste component of the taste element to be estimated may be selected according to the crop whose taste is to be estimated.

  Here, the relationship between the taste component amount of each taste element and the measurement data affecting the taste component amount of each taste element will be described in detail. As a taste component amount of each taste element, it is preferable to use a highly versatile component that can accurately estimate the taste regardless of the type of crop. As a result, the taste can be accurately estimated regardless of the type of crop. As a result, the application range of crops whose taste can be estimated is expanded, so that convenience can be greatly improved.

  First, the amount of sugar is used as the amount of the taste component indicating the quality of sweetness. The amount of sugar is generated by photosynthesis of crops and affects the respiration rate of crops. Therefore, the measurement data that affects the amount of sugar include accumulated sunshine hours [h], accumulated potassium in soil [mg / dry soil 100 g], the integrated value of the average temperature at night [° C.], and the integrated value of the average temperature during the day [° C.] may be used. In other words, the taste component amount estimation unit 11c uses the first regression equation to calculate the accumulated sunshine duration [h], the accumulated potassium amount in the soil [mg / 100 g of dry soil], the accumulated value of the average temperature at night [° C.], and the daytime The sugar content is estimated by substituting the integrated value [° C.] of the average temperature.

  Next, the amount of nitrate nitrogen and the amount of polyphenol are used as the amount of the taste component indicating the quality of bitterness. Agricultural crops absorb nitrogen into the body as nitrate nitrogen, but if the soil becomes excessively nitrogen and the crop absorbs nitrogen more than necessary, nitrogen remains in the crop. Therefore, as the measurement data affecting the nitrate nitrogen amount, the accumulated nitrogen amount in soil [mg / 100 g of dry soil] is preferably used. That is, the taste component amount estimation unit 11c estimates the nitrate nitrogen amount by substituting the data of the accumulated nitrogen amount in soil [mg / 100 g of dry soil] into the first regression equation. In addition, since the amount of polyphenol varies depending on the temperature and the amount of light, the integrated light amount [lx] and the integrated value of average temperature [° C.] may be used as measurement data that affects the amount of polyphenol. That is, the taste component amount estimation unit 11c substitutes the data of the integrated light amount [lx] and the integrated value of average temperature [° C.] into the first regression equation to estimate the polyphenol amount. And the sum of this amount of nitrate nitrogen and the amount of polyphenol is used for the estimation of the bitterness value mentioned later as a bitter total taste component amount.

  Next, the amount of citric acid is used as the amount of the taste component indicating the sour quality. Since the amount of citric acid gradually decreases with the generation of energy due to the action of the citric acid circuit, the elapsed time after flowering [h] is preferably used as measurement data that affects the amount of citric acid. That is, the taste component amount estimation unit 11c substitutes the data of the elapsed time after flowering [h] in the first regression equation and estimates the citric acid amount.

  Next, the amount of salt is used as the amount of taste components indicating the quality of salty taste. Since there is an agricultural product that stores salt absorbed from the external environment and the salt has a salty taste, it is preferable to use the accumulated amount of salt in soil [mg / 100 g of dry soil] as measurement data that affects the amount of salt. That is, the taste component amount estimation unit 11c substitutes the data of the accumulated soil salinity [mg / 100 g of dry soil] into the first regression equation to estimate the salinity.

  Next, the amount of amino acids is used as the amount of taste component indicating the quality of umami. Amino acids are produced by the action of nitrogen assimilation in photosynthesis, and crops directly absorb amino acids in the soil. Therefore, measurement data affecting the amount of amino acids include soil accumulated nitrogen amount [mg / 100 g dry soil], soil accumulated amino acid amount [mg / dry soil 100 g], accumulated sunshine time [h], soil accumulated amount. The amount of potassium [mg / 100 g of dry soil] and the integrated value of daytime average temperature [° C.] may be used. That is, the taste component amount estimation unit 11c adds the soil accumulated nitrogen amount [mg / dry soil 100g], the soil accumulated amino acid amount [mg / dry soil 100g], the accumulated sunshine time [h], Substituting data of accumulated potassium amount in soil [mg / 100 g of dry soil] and accumulated value of daytime average temperature [° C.], the amino acid amount is estimated.

  Next, the amount of decomposed pectin, the amount of amylose / amylopectin, or the amount of protein is used as the amount of the taste component indicating the quality of texture. First, since crops increase the softness due to degradation of intercellular substances called pectin over time, the elapsed time [h] after fruit set is used as measurement data that affects the amount of degraded pectin. Good. That is, the taste component amount estimation unit 11c estimates the decomposed pectin amount by substituting the data of the elapsed time [h] after ripening into the first regression equation. Since starch amylose / amylopectin is produced by photosynthesis, the measurement data affecting the amount of amylose / amylopectin include accumulated sunshine hours [h], average value of average night temperature [° C.], The integrated potassium amount [mg / 100 g of dry soil] and the integrated value of the average daily temperature [° C.] may be used. In other words, the taste component amount estimation unit 11c uses the first regression equation to calculate the accumulated sunshine hours [h], the accumulated value of average night temperature [° C.], the accumulated potassium amount in soil [mg / 100 g of dry soil], and the daytime. The amount of amylose / amylopectin is estimated by substituting the integrated value of average temperature [° C.]. Since protein is produced from amino acids and potassium is involved in regulating the activity of its organs, the measurement data that affect the amount of protein include accumulated nitrogen in soil [mg / 100 g of dry soil] The accumulated amino acid amount [mg / 100 g of dry soil], the accumulated potassium amount in the soil [mg / 100 g of dry soil], the integrated value of average daily temperature [° C.], and the integrated sunshine duration [° C.] may be used. That is, the taste component amount estimation unit 11c adds the soil accumulated nitrogen amount [mg / dry soil 100g], soil accumulated amino acid amount [mg / dry soil 100g], soil accumulated potassium amount [mg / The amount of protein is estimated by substituting data of 100 g of dry soil], integrated value of average daily temperature [° C.], and integrated sunshine duration [° C.]. Then, the product of the pectin content, the amylose / amylopectin content, the protein amount and the coefficient corresponding to the degree of influence of each component on the crops are added to obtain the total taste component amount of the texture, which will be described later. Use it to guess.

  Next, the amount of moisture is used as the amount of taste component indicating the quality of moisture. Since agricultural products are activated by the absorption of water when stress is applied, the measurement data that affects the amount of water includes the soil total moisture content [%] and soil total salt content [mg / 100 g dry soil]. Use it. That is, the taste component amount estimation unit 11c estimates the moisture content by substituting the data of the accumulated soil moisture amount [%] and the accumulated soil salt amount [mg / dry soil 100g] into the first regression equation. It becomes.

  Next, monoterpene or ester is used as the amount of taste component indicating the quality of the fragrance. Monoterpene is a component that exhibits a citrus scent and decreases with time, so the elapsed time [h] after fruit set is used as measurement data that affects monoterpene. That is, the taste component amount estimation unit 11c estimates the monoterpene amount by substituting the data of the elapsed time [h] after ripening into the first regression equation. Esters are components that exhibit the scent of general fruits and matsutake mushrooms. As the crop matures, over time, breathing becomes incomplete and alcohol is produced, which is produced by combining with organic acids in the crop. For this reason, it is preferable to use the elapsed time [h] after ripening as measurement data affecting the ester. That is, the taste component amount estimation unit 11c estimates the ester amount by substituting the data of the elapsed time [h] after ripening into the first regression equation. These monoterpenes or esters may be appropriately used depending on the crops whose taste is to be estimated.

  As described above, by using measurement data that has a large influence on the generation of the taste component amount according to the taste component amount of each taste element, it becomes possible to accurately estimate the taste component amount, and to improve the taste of crops. It can be estimated more accurately.

  The taste value estimation unit 12c uses the second regression equation read from the database 2 by the communication unit 1a and uses the second regression equation to estimate the taste component amount of each taste element corresponding to the taste component amount value estimated by the taste component amount estimation unit. It has a function as a taste value estimation means for estimating a value. Specifically, the taste value estimation unit 12c substitutes the taste component amount data of each taste element obtained by the taste component amount estimation unit 12c stored in the storage unit 1b in the second regression equation, The taste value of each taste element is estimated, and the obtained value is stored in the storage unit 1b. That is, when the taste value estimation unit 12c estimates the sweet taste value, the taste value estimation unit 12c substitutes the sugar content data obtained by the taste component amount estimation unit 11c into the second regression equation, A sweet taste value (sweet value) is estimated. When the taste value estimation unit 12c estimates the bitter taste value, the taste value estimation unit 12c substitutes the data of the total bitter taste component amount obtained by the taste component amount estimation unit 11c into the second regression equation. The bitter taste value (bitter taste value) is estimated. When the taste value estimation unit 12c estimates the sour taste value, the taste value estimation unit 12c substitutes the data of the citric acid amount obtained by the taste component amount estimation unit 11c into the second regression equation, and the acidity The taste value (sour value) is estimated. When the taste value estimation unit 12c estimates the salty taste value, the taste value estimation unit 12c substitutes the salt content data obtained by the taste component amount estimation unit 11c into the second regression equation to obtain the salty taste value. The taste value (salt taste value) is estimated. When the taste value estimation unit 12c estimates the taste value of umami, the taste value estimation unit 12c substitutes the amino acid amount data obtained by the taste component amount estimation unit 11c into the second regression equation to determine the umami taste. The taste value (umami value) is estimated. When the taste value estimation unit 12c estimates the taste value of the texture, the taste value estimation unit 12c substitutes the data of the total texture taste component amount obtained by the taste component amount estimation unit 11c into the second regression equation. Then, the taste value (texture value) of the texture is estimated. When the taste value estimation unit 12c estimates the taste value of moisture, the taste value estimation unit 12c substitutes the moisture content data obtained by the taste component amount estimation unit 11c into the second regression equation, and calculates the moisture content. The taste value (moisture value) is estimated. When the taste value estimation unit 12c estimates the scent taste value, the taste value estimation unit 12c substitutes the monoterpene or ester data obtained by the taste component amount estimation unit 11c into the second regression equation, The savory taste value (scent value) is estimated. The taste value of each taste element is a value indicating how close the taste component amount value of each taste element is to the target value. Here, the target value of the taste component amount of each taste element is appropriately set according to the crop whose taste is to be estimated. For example, if the crop whose taste is to be estimated is rice, the value of the amount of the taste component of each taste element of the “highest quality” rice that won the competition has only to be set as the target value. In addition, the target value is the value of the taste component amount of each taste element of rice that has been selling well in the past, and the value of the taste component amount of each taste element of rice that you have actually felt to be delicious. Also good. For other crops as well, the target value is the amount of taste component of each taste element of crops that have been sold well in the past, won the competition, or tasted delicious by eating on their own. Also good.

The taste estimation part 13c has a function as a taste estimation means for obtaining the estimated value of the taste of the crop. Specifically, the product of the coefficient according to the contribution degree of each taste element to the taste of the crop read out from the database 2 by the communication unit 1a and the taste value of each taste element obtained by the taste value estimation unit 12c is obtained. This product is added to get an estimate of the taste of the crop. That is, the sweetness value is M, the bitterness value is N, the sourness value is P, the salty value is Q, the umami value is R, the texture value is S, the moisture value is T, the scent value is U, the sweetness coefficient is a, the bitterness The estimated taste of a crop, where b is the coefficient of acidity, c is the coefficient of acidity, d is the coefficient of saltiness, e is the coefficient of umami, f is the coefficient of texture, g is the coefficient of moisture, and h is the coefficient of aroma. Vtest indicates the relationship represented by the following formula (1).
aM + bN + cP + dQ + eR + fS + gT + hU = Vtest (1)

  Here, with reference to FIG. 3, a method for deriving coefficients according to the degree of contribution of each taste element to the taste of the crop registered in the database 2 will be described in detail. FIG. 3 is an image diagram showing a list in which the taste value of each taste element with respect to the taste of the crop and the taste of the crop are given points. In this description, a method is shown in which simultaneous equations are formed using Equation (1), and the coefficients are determined by solving them to determine the degree of contribution of each taste element to the taste of the crop. Moreover, the case where rice is used for the crop to be estimated and sweetness, bitterness, umami, texture, and moisture are used as the taste elements of rice will be described as an example.

  First, a plurality of taste values of each taste element corresponding to the taste of the crop to be estimated and each taste are actually measured. Specifically, the rice is sampled, and the measured value VtestPV of the taste of rice and the sweetness value, the bitterness value, the umami value, the texture value, and the moisture value according to the measured value VtestPV of the taste are scored. As shown in FIG. 3, these scores are summarized in a table for each rice that has been eaten. Here, the actually eaten rice is different from the growing rice that is the subject of estimation, and is the rice that has been harvested in the past. In addition, it is possible to obtain the coefficient with high accuracy if the crop to be measured is the same type and variety as the crop to be estimated.

Next, the simultaneous equations are formed by substituting these points into the equation (1). That is, by substituting the actual taste value VtestPV of the rice taste into the estimated taste value Vteste of the crop of the formula (1), the sweetness coefficient a, the bitterness coefficient b, the umami coefficient e, the texture coefficient f, and the moisture content Using the coefficient g as a variable, the sweetness value M, the bitterness value N, the umami value R, the texture value S, the moisture value T, and the measured sweetness value, bitterness value, umami value, texture value, and moisture value score, respectively. substitute. Here, the connected equation is formed by a number one less than the number of taste elements to be used. In this example, since five taste elements of sweetness, bitterness, umami, texture, and moisture are used, four simultaneous equations are formulated. Specifically, simultaneous equations of the following formulas (2) to (5) are formed using data of actually eaten rice 1 to 4.
0.9a + 0.2b + 0.8e + 0.9f + 0.5g = 0.8 Formula (2)
0.6a + 0.4b + 0.5e + 0.7f + 0.6g = 0.7 Formula (3)
1.0a + 0.1b + 0.9e + 1.0f + 0.4g = 0.9 Formula (4)
0.4a + 0.4b + 0.3e + 0.1f + 0.6g = 0.2 Formula (5)
In addition to this, when the sweetness value, the bitterness value, the umami value, the texture value, and the moisture value become the maximum values, the simultaneous equation of the following equation (6) is established, in which the estimated value Vtest of the rice taste becomes the maximum value. Formula.
1.0a + 1.0b + 1.0e + 1.0f + 1.0g = 1.0 Formula (6)
Equation (6) is used to adjust the coefficient value so that when the sweetness value, the bitterness value, the umami value, the texture value, and the moisture value are the maximum values, the estimated value Vtest of the rice taste is also the maximum value. It is a formula.

  Next, the simultaneous equations of Equations (2) to (6) are solved to obtain the values of the sweetness coefficient a, the bitterness coefficient b, the umami coefficient e, the texture coefficient f, and the moisture coefficient g. If there are too many decimal places, rounding up or down is recommended.

  Next, from the measured rice taste data VtestPV and the actual rice data of the sweetness value, bitterness value, umami value, texture value, and moisture value corresponding to the taste measurement value VtestPV, the formula (2) Using the eaten rice data different from the eaten rice 1 to 4 used in the formula (5), repeating the same procedure, the sweetness coefficient a, the bitterness coefficient b, the umami coefficient e, the texture Coefficient f and moisture coefficient g. In this way, a plurality of sweetness coefficients a, bitterness coefficients b, umami coefficients e, texture coefficients f, and moisture coefficients g are obtained.

  Next, average values of the sweetness coefficient a, the bitterness coefficient b, the umami coefficient e, the texture coefficient f, and the moisture coefficient g are obtained. The average value of each of the sweetness coefficient a, the bitterness coefficient b, the umami coefficient e, the texture coefficient f, and the moisture coefficient g, and the sweetness value, bitterness value, umami value, texture value, and moisture value, respectively. Is substituted into the formula (1) to obtain an estimated value Vtest of the taste of rice. At this time, normalization is performed if the estimated rice taste estimate value Vtest does not coincide with the highest value of the estimated rice taste value Vtest. The normalization referred to here means a sweetness coefficient a, a bitterness coefficient b, an umami coefficient e, a texture coefficient f, so that the maximum value of the estimated rice taste value Vtest is a predetermined value. The average value of the coefficient of moisture g is adjusted. From the above, coefficients corresponding to the contribution of sweetness, bitterness, umami, texture, and moisture to the taste of rice are obtained. In this example, the coefficient according to the contribution of sweetness, bitterness, umami, texture, and moisture to the taste of rice was obtained, but even in the case of other crops, by using the same method, A coefficient corresponding to the degree of contribution of each taste element to the taste of the crop can be obtained.

  Then, with reference to FIG. 4, the processing operation | movement of the crop taste estimation apparatus 1 which concerns on Embodiment 1 of this invention is demonstrated. FIG. 4 is a flowchart showing the processing operation of the crop taste estimation apparatus according to the first embodiment of the present invention.

  First, the calculation unit 1c instructs the communication unit 1a to read out a coefficient corresponding to the degree of contribution of each taste element to the taste of the crop. And the communication part 1a reads and acquires all the coefficients required for the taste estimation of the crop from the 2nd area | region of the database 2 according to the request | requirement of the calculating part 1c, and preserve | saves it at the memory | storage part 1b. (Step S01)

  Subsequently, at least one selected from the basic taste elements of sweetness, bitterness, sourness, salty taste, and umami and at least 1 selected from the secondary taste elements of texture, moisture, and scent are calculated by the computing unit 1c. A first regression analysis of the relationship between the first reference value, which is the amount of taste component indicating the quality of each taste element of the species, and the second reference value, which is measurement data that affects the amount of taste component of each taste element Instructs to read the regression equation. And according to the request | requirement of the calculating part 1c, the communication part 1a reads and acquires all the 1st regression equations required for the taste estimation of the crop from the 3rd area | region of the database 2, and preserve | saves it at the memory | storage part 1b. To do. (Step S02)

  Subsequently, the calculation unit 1c sends to the communication unit 1a the third reference value that is the taste component amount of each taste element and is different from the first reference value, and the value of the taste component amount of each taste element is the target value. The second regression equation obtained by regression analysis of the relationship with the fourth reference value that is the taste value of each taste element indicating whether the taste elements are close to each other is instructed to be read out. And the communication part 1a reads and acquires all the 2nd regression equations required for the taste estimation of the crop from the 2nd area | region of the database 2 according to the request | requirement of the calculating part 1c, and preserve | saves it at the memory | storage part 1b To do. (Step S03)

  Subsequently, the calculation unit 1c instructs the communication unit 1a to read out measurement data at the location of the crop whose taste is to be estimated. And the communication part 1a reads and acquires measurement data from the 1st area | region of the database 2 according to the request | requirement of the calculating part 1c, and preserve | saves it at the memory | storage part 1b. (Step S04)

  Subsequently, the measurement data of the day necessary for estimation is read out from the storage unit 1b from the first regression equation acquired in step S02 by the taste component amount estimation unit 11c and the measurement data acquired in step S04, and the elapsed time Alternatively, the integrated value of the data from the starting date of the integration to the date to be estimated is obtained, the value of the taste component amount of each taste element at the time of measurement using the first regression equation is estimated, and this estimated value is Save in the storage unit 1b. (Step S05)

  Subsequently, the second regression equation acquired by the taste value estimation unit 12c in step S03 and the estimated value of the taste component amount of each taste element obtained by the taste component amount estimation unit 11c in step S05 are read from the storage unit 1b. Then, the taste value of each taste element corresponding to the value of the taste component amount of each taste element is estimated using the second regression equation, and the estimated value is stored in the storage unit 1b. (Step S06) At this time, the crop taste estimation device 1 repeats the processing operations of Step S05 and Step S06 by the number of necessary taste elements according to the crop whose taste is to be estimated.

  Subsequently, the taste estimation unit 13c multiplies the coefficient according to the contribution degree of each taste element to the taste of the crop acquired in step S01 and the taste value of each taste element obtained by the taste value estimation unit 12c in step S06. Ask for. Repeat this calculation for as many taste factors as you need, depending on the crop you want to guess. And the estimated value of the taste of farm products is calculated by adding the products of the calculated taste elements. (Step S07) As described above, the crop taste estimation device 1 performs the processing operation of steps S01 to S07, so that an estimated value of the taste of the crop can be obtained.

  As described above, the crop taste estimation device 1 according to the present embodiment is a device that estimates the taste of a crop, and refers to the database 2 and uses basic taste elements of sweet taste, bitter taste, sour taste, salty taste, and umami taste. A first reference value that is an amount of a taste component indicating the quality of each taste element selected from at least one selected taste and secondary taste elements of texture, moisture, and aroma; The first regression equation obtained by regression analysis of the relationship with the second reference value, which is measurement data that affects the amount of taste component, is read out, and the taste component of each taste element at the time of measurement using the first regression equation The taste component amount estimating means (the taste component amount estimating unit 11c) for estimating the amount value, and the database 2, the third reference value that is the taste component amount of each taste element and is different from the first reference value And how close the taste component amount of each taste element is to the target value. The second regression equation obtained by regression analysis of the relationship with the fourth reference value, which is the taste value of each taste element indicating whether or not, is used to determine the taste component amount estimation means (the taste component amount using the second regression equation) Taste value estimation means (taste value estimation unit 12c) for estimating the taste value of each taste element corresponding to the value of the taste component amount of each taste element estimated by the estimation unit 11c), the contribution of each taste element to the taste of the crop The product of the coefficient corresponding to the taste value and the taste value of each taste element estimated by the taste value estimation means (taste value estimation unit 12c) is obtained, and the product of each taste element is added to obtain the estimated value of the taste of the crop Guessing means (taste guessing unit 13c). Therefore, since the taste component amount is estimated using the second reference value that is measurement data that affects the taste component amount of each taste element of the crop, a sample for taste estimation is collected from the growing crop. Without guessing the taste. In addition, one kind of basic taste element that contributes to the determination of the taste of crops and one kind from secondary taste elements are selected, and the taste is evaluated numerically as a taste value. can do.

(Embodiment 2)
Next, with reference to FIG. 5, the structure of the crop taste estimation apparatus 10 according to Embodiment 2 of the present invention will be described. FIG. 5 is a block diagram showing a crop taste estimation device according to Embodiment 2 of the present invention together with a database.

  The crop taste estimation device 10 includes a communication unit 1a, a storage unit 1b, and a calculation unit 1c, similarly to the crop taste estimation device 1 according to the first embodiment. The crop taste estimation apparatus 10 according to the second embodiment is different from the crop taste estimation apparatus 1 according to the first embodiment in that it further includes a position information acquisition unit 1d. Hereinafter, a description will be given focusing on differences from the first embodiment.

  As shown in FIG. 5, the position information acquisition unit 1d acquires position information of a crop that is to be estimated, and has a function as a position information acquisition unit that transmits the position information to the calculation unit 1c. Based on this position information, the calculation unit 1c determines which location measurement data is to be acquired, and reads out the measurement data of the location determined by the communication unit 1a by the calculation unit 1c (taste component amount estimation unit 11c). Instruct. The communication unit 1a reads out and acquires measurement data of a location based on the position information measured by the position information acquisition unit 1d from the first region of the database 2 in response to a request from the calculation unit 1c (taste component amount estimation unit 11c). And stored in the storage unit 1b. And the calculating part 1c (taste component amount estimation part 11c) estimates the taste component quantity of each taste element corresponding to the measurement data based on the positional information which the positional information acquisition part 1d measured. Examples of the position information acquisition unit 1d include devices such as GPS (Global Positioning System). In addition, when the crop taste estimation device 10 according to the present embodiment is mounted on a device equipped with a GPS function such as a multi-function mobile phone (smartphone), it is possible to obtain position information by visiting the place of the crop to be measured by the measurer The position information of the crop that the unit 1d wants to measure can be easily acquired.

  As described above, the crop taste estimation device 10 according to the present embodiment further includes the position information acquisition unit (position information acquisition unit 1d) that acquires the position information of the crop, and the taste component amount estimation unit (taste component amount). The estimation unit 11c) estimates the taste component amount of each taste element corresponding to the measurement data based on the position information measured by the position information acquisition unit (position information acquisition unit 1d). Therefore, the accuracy of the estimation of the taste of the crop can be further improved by using the measurement data based on the position information of the crop without performing measurement such as instrument analysis that requires specialized knowledge.

(Embodiment 3)
Next, with reference to FIG. 6, the structure of the crop taste estimation apparatus 100 according to Embodiment 3 of the present invention will be described. FIG. 6 is a block diagram showing a crop taste estimation device according to Embodiment 3 of the present invention together with a database.

  The crop taste estimation device 100 includes a communication unit 1a, a storage unit 1b, and a calculation unit 1c, similarly to the crop taste estimation device 1 according to the first embodiment. The crop taste estimation apparatus 100 according to the third embodiment is different from the crop taste estimation apparatus 1 according to the first embodiment in that it further includes a display unit 1e. Hereinafter, a description will be given focusing on differences from the first embodiment.

  The display unit 1e is connected to the calculation unit 1c, and has a function of displaying the estimated value of the taste of the crop estimated by the taste estimation unit 13c. As the display method, the estimated value of the taste of the crop itself may be displayed, or the color may be displayed based on the estimated value of the taste of the crop, or the character may be displayed based on the estimated value of the taste of the crop. Also good. Moreover, you may make it display combining these display methods.

  Here, the display method of the display unit 1e will be described in detail. First, the display method in the case of displaying in color based on the estimated value of the taste of the crop will be described. The display part 1e sets the color to display according to the estimated value of the taste of agricultural products beforehand. Specifically, when the estimated value of the taste of the crop is 0 to 0.2, blue is displayed, when it is 0.2 or more and less than 0.4, green is displayed, and 0.4 to less than 0.6 is displayed. Is set so that yellow is displayed, orange is displayed when 0.6 or more and less than 0.8, and red is displayed when 0.8 or more and 1.0 or less. By setting in this way, for example, when the estimated value of the taste of the crop is 0.7, an orange color is displayed. If the number of colors displayed by the display unit 1e is increased and the range of the estimated value of the taste of the crop corresponding to the color is narrowed, the taste of the crop can be accurately expressed.

  Next, the display method in the case of displaying characters based on the estimated value of the taste of the crop will be described. The display part 1e sets the character to display according to the estimated value of the taste of agricultural products beforehand. Specifically, when the estimated value of the taste of the crop is 0 to 0.2, “Immature” is displayed, and when it is 0.2 or more and less than 0.4, “Still to eat” is displayed. When it is 4 or more and less than 0.6, it will display “Easy to eat after a while”, when it is 0.6 or more and less than 0.8, it will display “When it is a little more”, and when it is 0.8 or more and 1.0 or less, It is set to display "Now it's about to eat". By setting in this way, for example, when the estimated value of the taste of the crop is 0.7, “When you have a little more time to eat” will be displayed. Note that if the number of characters displayed on the display unit 1e is increased and the range of the estimated value of the taste of the crop corresponding to the character is narrowed, the taste of the crop can be expressed with high accuracy.

  Moreover, you may comprise so that the range which the display part 1e displays can be selected. Specifically, it may be configured to display one estimated value for one field or field, or may be configured to display the estimated value for each mesh by dividing the field or field with a mesh. The estimated values of a plurality of farm fields and farm products may be displayed at a time. In addition, when displaying one estimated value about one field or a field, what is necessary is just to have a sensor which acquires at least 1 set, soil component information, growth condition information, etc. in a field. In addition, when an estimated value is displayed for each mesh by dividing the farm field or the field with a mesh, a sensor that acquires soil component information, growth status information, and the like may be provided for each mesh. In addition, when displaying estimated values of a plurality of fields and fields at a time, if the fields and fields are close to each other, one set of representative fields and fields may be used to improve accuracy or fields and fields. If there is a remote location, there may be a sensor that acquires soil component information, growth status information, etc., one set for each field or field.

  Further, the display unit 1e may have a history display function. The history display is to display an estimated value of the taste of the crop before the measurement time. In this case, various data at the time of estimation are read out from the database 2 and acquired, and the crop taste estimation device 100 performs the processing operation for estimating the taste of the crop shown in the first embodiment to obtain the estimated value of the taste of the crop. Can be displayed.

  Here, the history display of the display unit 1e will be described in detail with reference to FIGS. FIG. 7 is an example in which the display unit of the crop taste estimation device according to the third embodiment of the present invention displays the history in a bar shape. FIG. 8 is an example in which the display unit of the crop taste estimation device according to the third embodiment of the present invention displays the history in a graph.

  As shown in FIG. 7, it is possible to grasp the transition of the estimated value of the taste of the crop by displaying the history of the estimated value of the taste of the crop in a bar shape on the display unit 1e. In this example, the history of the estimated value of the taste of the crop is displayed in the order of 3 days ago, 2 days ago, yesterday and today from the left side of the figure.

  Similarly, as shown in FIG. 8, by displaying the history of the estimated value of the taste of the crop in the form of a graph on the display unit 1e, it is possible to grasp the transition of the estimated value of the taste of the crop. In this example, the date [day] is displayed on the horizontal axis, and the estimated value of the taste of the crop is displayed on the vertical axis. In the example of FIG. 8, since the history of the estimated value of the taste of the crop is continuously displayed, it is possible to grasp the transition of the estimated value of the taste of the crop with higher accuracy than in the example of FIG. Is possible.

  As described above, the crop taste estimation device 100 according to the present embodiment further includes the display unit (display unit 1e) that displays the estimated value of the taste of the crop. Therefore, it is possible to easily inform the user of the estimated taste value, so that convenience can be improved.

  The present invention has been described based on the embodiments. The embodiments are exemplifications, and it will be understood by those skilled in the art that various modifications can be made to combinations of the respective constituent elements and processing processes, and such modifications are within the scope of the present invention. . Accordingly, the description and drawings herein are to be regarded as illustrative rather than restrictive.

  For example, the crop taste estimation device 100 according to the third embodiment further includes a position information acquisition unit 1d of the crop taste estimation device 10 according to the second embodiment, and the device is configured to be mounted on a multi-function mobile phone. Also good. In this case, all the functions are installed in one device, and it is possible to estimate the taste of the crop more easily.

DESCRIPTION OF SYMBOLS 1,10,100 ... Taste estimation apparatus of agricultural products, 1a ... Communication part, 1b ... Memory | storage part, 1c ... Calculation part, 1d ... Position information acquisition part, 1e ... Display part, 11c ... Taste component amount estimation part, 12c ... Taste Value estimation part, 13c ... taste estimation part, 2 ... database.

Claims (9)

A device for estimating the taste of agricultural products,
Select from at least one selected from the basic taste elements of sweetness, bitterness, sourness, salty taste, and umami, and secondary taste elements of texture, moisture, and aroma using measurement data that is environmental information surrounding the crop A taste component amount estimation means for estimating a value of a taste component amount indicating the quality of each of at least one kind of taste element,
Using the taste component amount estimated by the taste component amount estimation means, the taste value of each taste element indicating how close the taste component amount value of each taste element is to the target value is estimated Taste value estimation means to perform,
A taste estimator for a crop, comprising: a taste estimator that obtains an estimated value of the taste of the crop using the taste value of each taste element estimated by the taste value estimator.   The taste component amount estimation means obtains a first regression equation obtained by regression analysis of a relationship between a first reference value which is a taste component amount indicating the quality of each taste element and a second reference value which is the measurement data. The agricultural product taste estimation apparatus according to claim 1, wherein the value of the taste component amount is estimated.   The taste value estimation means is a taste component amount indicating the quality of each taste element, and a third reference value different from the first reference value and how close the third reference value is to the target value The crop taste estimation device according to claim 2, wherein the taste value is estimated using a second regression equation obtained by regression analysis of a relationship with a fourth reference value that is a taste value of each taste element indicating the taste value.   The crop taste estimation device according to claim 3, wherein the first to fourth reference values are past values. The taste component amount of each taste element is
Sweetness is sugar content,
Bitterness is nitrate nitrogen content and polyphenol content Acidity is citric acid content
Saltiness is the amount of salt,
Umami is amino acid content,
Pectin content, amylose / amylopectine content, or protein whose texture has been degraded,
Moisture is the amount of moisture,
The scent is a monoterpene or an ester, The crop taste estimation device according to any one of claims 2 to 4. The value of the sugar content is a regression of the relationship between the first reference value of the sugar content and the accumulated sunshine hours, the accumulated potassium amount in the soil, the accumulated value of the average temperature of the night, and the accumulated value of the average temperature of the day. Inferred using the analyzed first regression equation,
The value of the nitrate nitrogen amount is estimated using the first regression equation obtained by regression analysis of the relationship between the first reference value of the nitrate nitrogen amount and the accumulated nitrogen amount in the soil,
The value of the polyphenol amount is estimated using the first regression equation obtained by regression analysis of the relationship between the first reference value of the polyphenol amount, the integrated light amount and the integrated value of the average temperature,
The value of the citric acid amount is estimated using the first regression equation obtained by regression analysis of the relationship between the first reference value of the citric acid amount and the elapsed time after flowering,
The value of the salinity is estimated using the first regression equation obtained by regression analysis of the relationship between the first reference value of the salinity and the accumulated salinity in the soil,
The value of the amino acid amount is the first reference value of the amino acid amount, the accumulated nitrogen amount in the soil, the accumulated amino acid amount in the soil, the accumulated sunshine time, the accumulated potassium amount in the soil, and the accumulated value of the average daily temperature. Inferred by using the first regression equation obtained by regression analysis of the relationship,
The amount of decomposed pectin, the amount of amylose / amylopectin, or the value of protein is the relationship between the first reference value of the amount of decomposed pectin and the elapsed time after fruiting, the first amount of the amount of amylose / amylopectin. Relationship between reference value and accumulated sunshine hours, accumulated value of average night temperature, accumulated potassium amount in soil, accumulated value of average daily temperature, or first reference value and accumulated nitrogen amount in soil The estimated amount of accumulated amino acid in soil, the accumulated amount of potassium in soil, the accumulated value of daytime average temperature, and the first regression equation obtained by regression analysis of the relationship with the accumulated sunshine hours,
The value of the moisture content is estimated using the first regression equation obtained by regression analysis of the relationship between the first reference value of the moisture content and the accumulated moisture content in the soil and the accumulated salt content in the soil,
The value of the monoterpene or the ester amount is a regression of the relationship between the first reference value of the monoterpene and the elapsed time after ripening, or the relationship between the first reference value of the ester amount and the elapsed time after ripening. The crop taste estimation device according to claim 5, which is estimated using the analyzed first regression equation. Further comprising position information acquisition means for acquiring position information of the crop,
The said taste component amount estimation means estimates the value of the taste component amount of each said taste element corresponding to the measurement data based on the positional information measured by the said positional information acquisition means. Description of the crop taste estimation device.   The crop taste estimation device according to any one of claims 1 to 7, further comprising display means for displaying an estimated value of the taste of the crop. Computer to guess the taste of crops,
Select from at least one selected from the basic taste elements of sweetness, bitterness, sourness, salty taste, and umami, and secondary taste elements of texture, moisture, and aroma using measurement data that is environmental information surrounding the crop A taste component amount estimation means for estimating a value of a taste component amount indicating the quality of each of at least one kind of taste element,
Using the taste component amount estimated by the taste component amount estimation means, the taste value of each taste element indicating how close the taste component amount value of each taste element is to the target value is estimated Taste value estimation means to perform,
Taste estimation means for obtaining an estimated value of the taste of the crop using the taste value of each taste element estimated by the taste value estimation means;
Crop taste estimation program to function as.

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